AIMC Topic: Meningeal Neoplasms

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Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging.

European radiology
OBJECTIVES: Preoperative, noninvasive prediction of the meningioma grade is important because it influences the treatment strategy. The purpose of this study was to evaluate the role of radiomics features of postcontrast T1-weighted images (T1C), app...

The diagnostic value of texture analysis in predicting WHO grades of meningiomas based on ADC maps: an attempt using decision tree and decision forest.

European radiology
OBJECTIVES: The preoperative prediction of the WHO grade of a meningioma is important for further treatment plans. This study aimed to assess whether texture analysis (TA) based on apparent diffusion coefficient (ADC) maps could non-invasively classi...

Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI.

European radiology
OBJECTIVES: Magnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine ...

Development of a deep convolutional neural network to predict grading of canine meningiomas from magnetic resonance images.

Veterinary journal (London, England : 1997)
An established deep neural network (DNN) based on transfer learning and a newly designed DNN were tested to predict the grade of meningiomas from magnetic resonance (MR) images in dogs and to determine the accuracy of classification of using pre- and...

eNAL++: a new and effective off-line correction protocol for rotational setup errors when using a robotic couch.

Journal of applied clinical medical physics
Cone-beam CTs (CBCTs) installed on a linear accelerator can be used to provide fast and accurate automatic six degrees of freedom (6DoF) vector displacement information of the patient position just prior to radiotherapy. These displacement correction...

A multiresolution clinical decision support system based on fractal model design for classification of histological brain tumours.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Tissue texture is known to exhibit a heterogeneous or non-stationary nature; therefore using a single resolution approach for optimum classification might not suffice. A clinical decision support system that exploits the subbands' textural fractal ch...

[Radiosurgery of benign intracranial lesions. Indications, results , and perspectives].

Revue medicale suisse
Stereotactic radiosurgery (SRS) is a non-invasive technique that is transforming the management of benign intracranial lesions through its precision and preservation of healthy tissues. It is effective for meningiomas, trigeminal neuralgia (TN), pitu...

An MRI-based deep transfer learning radiomics nomogram for predicting meningioma grade.

Scientific reports
The aim of this study was to establish a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features to predict meningioma grade. Three hundred forty meningiomas from one hospital composed the training set, and 102 meningiomas fr...

Machine Learning Analysis of Single-Voxel Proton MR Spectroscopy for Differentiating Solitary Fibrous Tumors and Meningiomas.

NMR in biomedicine
Solitary fibrous tumor (SFT), formerly known as hemangiopericytoma, is an uncommon brain tumor often confused with meningioma on MRI. Unlike meningiomas, SFTs exhibit a myoinositol peak on magnetic resonance spectroscopy (MRS). This study aimed to de...